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so the researchers really looking at a puzzle that we observed um in terms of new technologies being introduced into
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the market but significant differences in terms of how fast they're able to reach mainstream adoption and disrupt
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existing markets existing players so we we observe that in the printer space injet printers came about quickly took
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over the dot matrix we look at stdv it took decades for it to be mainstream adoption and then we look at things like
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the segue or the Palm uh types of Technologies which either created some value or never really reached mainstream
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adoption so so the question was really what explains that why some technologies are introduced and immediately supplant
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existing Technologies whereas others take decades or sometimes don't really reach mainstream adoption and so what we
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did was we we we tried to find a context where um we could observe enough variation in terms of how quick or slow
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these technology adoption patterns were but helped us to control for a lot of confounding effects as well and so the
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idea was to find a natural experiment so to speak and uh that helps us to ensure
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that the source of variation is not driven by some systematic effects which may make inferences more problematic so
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what we did was we we came across you know I had some experience in the semiconductor industry we came across
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the semiconductor lithography technology as as a setting that we could study this
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question um what is interesting about this setting is this is kind of the engine behind more slow or any progress
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in semiconductors over the last 40 years has been fueled by lithography technology so it's it's important we
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know it's fast-paced and and we thought that this is where we may want to study
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this question and so the research design looked at the semicon lithography and 10
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different new technologies that were introduced in that industry over a 40-year period um you know we
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interviewed about 30 industry experts to try to get a sense for what's driving
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these patterns we collected data on Technologies markets uh Industries um in terms of really trying
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to understand the factors that may be explaining this difference and uh what we found was was interesting you know a
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lot of the research and practice focuses on on on new technologies and how it interacts with the markets and look at
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is the technology better than what's available now or it's not and that explains whether it's going to reach
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mainstream adoption what we found was that's sort of part of the explanation
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and actually in our case a very small part of the explanation we found that the bigger explanation was not focusing
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on the new technology and the market but looking at the technology ecosystem which is to means that how is the
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technology created what are the different elements that make up the technology so think about batteries
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making electric cars but also how is the technology used by the users in terms of
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other elements or complimentary Technologies and services think about electric car and charging stations and
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garages who can fix that electric cars and and not only that uh that you need to look at the technology ecosystem for
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both the new technology and the old technology so what we set out is you know we want
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to explain this variance what we see from our our our fieldwork and our interviews that by
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looking at the technology itself the resolution in terms of fast versus slow is not going to be very good let's look
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at the ecosystem and so as as part of the research we collected data that systematically identifies each
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technology in terms of its ecosystem and and compared in terms of the existing technology and what we found was because
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when we think about the new technology there's sometimes what we call an ecosystem emergence challenge which
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means the technology is ready but the ecosystem still needs some Investments like the charging infrastructure for
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electric car for it to reach mainstream adoption and we call that ecosystem emergence challenge but we also see in
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the old technology sometimes you can extend that technology by improvements in components or these complimentary
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elements so think about the gasoline cars you know nobody thought 10 15 years ago that they will be going at 30 m per
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gallon or even 40 m per gallon but improvements in engines improvements in fuels allowed the cars to get to that
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point you think about the hybrid versus electric car uh sort of discussion where
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the hybrid cars introduced were able to grow market share much faster than electric cars and the main difference is
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the hybrid cars could plug and play there wasn't an emergence challenge in the ecosystem electric cars had these
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emergence challenges by creating the infrastructure by having these charging stations in place so at the end of the
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day after doing this research we were able to document fairly clearly that if one were to understand the likelihood
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that the new technology is going to come and immediately disrupt the marketplace
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versus it may take much longer or may not happen you have to look at the new technology ecosystem in terms of its
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emergence Challenge and the old technology ecosystem in terms of the extension opportunity and it's really
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The Joint consideration of these two factors that explain whether you will see a fast technology
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takeoff or a technology that will never reach mainstream adoption you know the research presents
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some very interesting takeaways for managers for uh policy makers for investors into technology companies and
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also for users of Technologies whether you're consumers or businesses so if you
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are a manager of a firm whether it's a new startup or it's an established firm
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you always have to think about sort of resources that you have to allocate towards new technologies and how you
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transition between an existing to a new technology so think about Kodak shifted from the chemical based to the digital
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photography or you think about Netflix moving from a DVD rental business to the online streaming business and so what we
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find is you know managers can use this framework to set realistic expectation in terms of both whether and when to
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invest in new technologies and it may sometimes actually make sense and they will generate more shareholder value or
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destroy less shareholder value uh by focusing on the existing Technologies than kind of going all out and pushing
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for the new technology from an Investor's perspective again it sets realistic expectations uh when you are
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investing in technology companies in the ability to create value over time you know sometimes the expectations could be
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2 years but as we know anecdotally it often takes much longer than that for new technologies to create value users
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you know same same sort of decisions whether you're a business or a consumer
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you're always up against the decision should we go for the latest and greatest
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or should we wait until until the new technology is developed to a point where it creates value and so having the
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sensibility in terms of existing ecosystem and new ecosystem can help them make more optimal decisions policy
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makers I mean this is a big question in terms of the role of policy in shaping technological progress
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and we know that a lot of economic progress that we see is driven by improvements in technology so if you run
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into a scenario where the new technologies are not emerging at the rate where you expect them to to to to
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sort of emerge you know that has a downside in terms of jobs in terms of economic output in terms of GDP growth
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so thinking more in terms of ecosystem both for the new technology and the existing technology helps all of these
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actors get better Returns on the investment get less surprises in terms of expectations you know that was the
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original intention in terms of our ability to forecast uh how Technologies are going
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to evolve over time and the title of the paper has S curves as a way people have
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thought about it and there two different ways people thought about forecasting Technologies one is thinking about the
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performance improvements of the Technologies and we know that those performance trajectories tend to be in s
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shaped early stage you invest a lot but you're not getting improvements then
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there's a takeoff where you expect most of the market takeoff and then there's
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maturity similarly like that there's an adoption esurf which is the early stage
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of the technology the users who are going to be going after it are not the ones who really care about the total
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value but they just like the new technology that's a very small part of the market the mainstream users are
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looking to see what's the main value proposition not just technology being new and that depicts an s-shaped
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distribution as well so what we what we were up against is these existing Frameworks and tools which has this
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nonlinear s-shaped Dynamics and what we are able to show through this ecosystem based sensibility is that the pattern of
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these Evolutions both in terms of Technology Improvement and market adoption may not fit well with a smooth
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s shaped trajectories and they often could be very nonlinear patterns could be very discontinuous patterns and
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having the sensibility can help you predict right whether the S shapes are going to be smooth and quick or these
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may not be s shaped and they may actually get resolved in a much longer time frame and and you know where I'm
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going with this project is exactly along those lines is to have um a a model and
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an approach that helps us to improve technology forecasting in fact one of the things I
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was surprised um when we were doing this research is um the amount of resources that both companies and governments were
00:10:02
expending into these new technologies often running into hundreds and billions of dollars as you know semiconductor
00:10:08
industry is very Capital intensive and despite these resources and expectations they have well documented industry road
00:10:15
maps that say these new technologies are going to be hitting mainstream 3 to 5 years more often than not they would
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take double the amount of time or in many cases never take off at all um and that's something that I was not some not
00:10:31
expecting as much I was expecting some of it but not at the scale that I observe the other issue that I thought
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was particularly surprising is you know we kind of write off existing Technologies and old Technologies you
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know the the cell phones nobody's going to use it everybody's going to shift to
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let's say tablets on and a newer newer ways to do things um but seemingly geriatric
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Technologies uh continue to create a lot of value for a very long time and that was counterintuitive for what we
00:11:01
expected to see in this result especially in an industry which is being driven by Moors
00:11:09
law so I think from a firm perspective as we said you know the big implication is thinking about resource allocation
00:11:15
and whether it makes sense for firms to go all out on these new technologies at the rate that they expect to invest um
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the other implication from a firm perspective is timing um you know in a related study I was able to document
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that you know first mover Advantage as we often claim in many technology settings turns out not to be the case
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and in fact we were able to show significant first mover disadvantage in Technologies where the ecosystem
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emergence challenge was so high that first movers couldn't create any value so issues of timing was important
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another issue that I think uh you know managers and uh investors need to think about is a technology is seldom a single
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artifact or a single technology element it is the ecosystem and that makes it very difficult for firms to control all
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the elements that go into creating value from the technology so one sensibility that you know I'd like to share with uh
00:12:12
the managers and and investors is as we think about evaluating technological opportunities it's not enough to look at
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just the focal technology whether it's the phone or it's a computer or it's the
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car is to think about the ecosystem what are the elements that go into it and could you orchestrate the ecosystem
00:12:28
system in a way that mitigates these potential downsides of the users not deriving value when the technology is
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ready um you know what was uh particularly interesting from our study was every time the new technology was
00:12:42
introduced that technology was actually Superior in terms of performance but despite that it never reached mainstream
00:12:49
adoption and the explanation was really rooted in the ecosystem of the new technology and the ecosystem of the old
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technology we had a we had a case where new techn ology came in three times four
00:13:00
times Superior performance but a critical element that the users need to use so think about you have a camera as
00:13:07
this technology but you need the film but the film that is you available with this new technology is not as good for
00:13:14
you to get the full potential of this new camera so users don't really see an
00:13:19
incentive so you're coming up with the best camera but with a below par film
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and the total value proposition is not there in other cases the Technologies are fairly increment m al but they're
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able to Plug and Play like a hybrid car for example and that doesn't feel that
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doesn't see these sort of resistances and fictions in the marketplace uh from
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a venture capital perspective again as we think about these entrepreneurial ecosystems as a way for Venture capitals
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to to sort of get in and create value um again you know you cannot just localize
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on a single firm as a basis of investment as a basis of value creation if you think about ebooks as a case in
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point U you know a lot lot of venture capital Investments companies like e Inc back in the day were funded through a
00:14:03
lot of VC money both uh private Venture Capital but also corporate Venture Capital um but it wasn't about the ink
00:14:11
itself it was about content it was about the reader it was about Amazon's business model that had to bring the
00:14:17
solution together so as we think about the opportunities for venture capital for any given technology it's not enough
00:14:22
to just focus on that technology but thinking about the broader ecosystem and how one could control and dve
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progress through the ecosystem important uh misperception that you see quite often is you know
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fairly optimistic aggressive expectations about new technologies and some people call it Hypes and some
00:14:46
people you know refer to it as hype Cycles um and I think we're able to kind
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of identify reasons of those Hypes and just calling them as a hype is not enough in in my
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view is to understanding the factors that drive the hype and then you know using that as a basis of making good
00:15:05
decisions um so you know there was clearly very high expectations in in our research context that these Technologies
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would reach mainstream adoption we going to be investing Millions if not billions
00:15:16
of dollars into into these Technologies but it turned out those Investments were
00:15:21
in vain because either the ecosystem didn't emerge at the right time or at the cost that made it attractive for the
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users the second sort of uh misperception is again this um you know very dramatic
00:15:35
views about the decline of the old Technologies and this you know framing of the world as a world of disruption
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and all the values going to be created through creative destruction coming from new technologies I you know we do feel
00:15:48
that that's that part of the world is a bit oversold and in fact uh a lot of the
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value uh in semiconductors in many of the other industries that I've studied
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is is been created through continued Innovations and finding Market opportunities through existing
00:16:04
Technologies and we found that in our context as well a very interesting company called ultratech has been in
00:16:10
this industry for 40 years that we have studied this industry it has never been at The Cutting Edge of the technology
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like some of its peers who entered and exited in a fairly short span and this company has existed for a very long time
00:16:24
and still creating a lot of shareholder value so I think I think those perceptions in terms of you know a very
00:16:31
optimistic view of of the new technologies and a very pessimistic view of the value proposition in the old
00:16:38
technology is something that we think needs to be more balanced as opposed to these extreme
00:16:47
views so we are certainly not the first ones to study new technologies and the ability to create value in the
00:16:54
marketplace um but I do think that we are one of the first if if not the first to bring to attention the important role
00:17:02
of the ecosystem in the way the technology gets developed and commercialized and to really understand
00:17:08
how these Technologies transition and Technology Dynamics we think it's not enough to just take the ecosystem
00:17:13
perspective but look at both the new technology ecosystem and the old technology ecosystem it's really
00:17:19
bringing these two ecosystems together as an analytical framework can generate a lot more valuable insights in our view
00:17:26
and we showed that in our research by just looking at the new technology on its own you know the other sort of
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aspect of the research which I think you know I'm particularly proud of is the
00:17:37
approach that we took to study this question it was you it required almost two years of field work we interviewed
00:17:44
more than 30 industry practitioners managers Consultants from a variety of roles in the ecosystem and that took a
00:17:52
lot of time but we felt that you know we were able to generate a set of robust findings that made that effort
00:18:00
worthwhile something that we don't often see in a lot of management research so this was in my view you know
00:18:10
probably a first step for me to try to think about how one could make better decisions in terms of new technologies
00:18:18
and provide a framework that could guide that uh decision- making I think we're
00:18:23
still not quite at the point I would like us to be in terms of our ability to forecast Technologies both existing and
00:18:31
new and and to kind of take a more probabilistic view that can guide managerial decision- making Venture
00:18:38
Capital Investments and even how policy makers think about so what I'm I I'm
00:18:42
sort of moving on is to take the issue of Technology forecasting more seriously and anecdotal evidence suggests that
00:18:50
we're actually very poor technology forecasters I mean there's not a lot of
00:18:54
uh documentation in terms of accuracy of forecasting but if you look at some of the the big technology forecast that
00:19:01
come out nobody tracks them which is a problem of course but anecdotally you can tell that you know a vast majority
00:19:08
of them turn out to be wrong either in terms of missing the timing completely or missing the new technologies that
00:19:16
nobody predicted 3 to 5 years ago um so my goal is to think about the theoretical logic but also think about
00:19:23
an estimation modeling approach that helps us to predict new technologies in a way that we have not able to do so far
00:19:32
and I'm starting with the auto sector which which is going through some fairly
00:19:37
interesting uh shifts at the moment as you've shifted from the electrification
00:19:42
now we're talking about autonomous cars we have the technology companies coming
00:19:46
into it in addition to the auto sector um and so I think it's it's an environment that presents a lot of
00:19:52
uncertainty but also a lot of variability in terms of technological choices being pursued so I'm working on
00:19:58
a project where uh we will try to create good forecasting models and hopefully we
00:20:05
can show the accuracy of these forecasting models in explaining many of the Technologies being pursued in the
00:20:11
auto sector and the hope is to kind of take that as a template and apply it in other technology settings you know think
00:20:18
about internet of things think about variable Technologies think about financial Technologies and we could
00:20:24
scale this model to make better technology forecast [Music]